Articles | Volume 17, issue 21
https://doi.org/10.5194/gmd-17-7569-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-7569-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| Highlight paper
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30 Oct 2024
Model description paper | Highlight paper |
| 30 Oct 2024
| 30 Oct 2024
A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting
Alessandro Maissen
CORRESPONDING AUTHOR
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
Frank Techel
WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
Michele Volpi
Swiss Data Science Center, ETH Zurich and EPFL, Zurich, Switzerland
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Editorial statement
Operational avalanche forecasting has so far been done almost exclusively by human forecasters. For the first time, an automated machine learning approach allows to reach forecasting skills close to human forecasters.
Operational avalanche forecasting has so far been done almost exclusively by human forecasters....
Short summary
By harnessing AI models, this work enables processing large amounts of data, including weather conditions, snowpack characteristics, and historical avalanche data, to predict human-like avalanche forecasts in Switzerland. Our proposed model can significantly assist avalanche forecasters in their decision-making process, thereby facilitating more efficient and accurate predictions crucial for ensuring safety in Switzerland's avalanche-prone regions.
By harnessing AI models, this work enables processing large amounts of data, including weather...
